Tips for improving customer satisfaction (CSAT)
Read this blog for tips on how you can overcome many of the challenges that surround CSAT and improve your organization's ability to please its custom...
December 28, 2016
You’ve no doubt heard this statistic before: It costs 5X as much to attract a new customer as it does to retain one. Yet research shows that the majority of organizations are still prioritizing customer acquisition over retention, with fewer than half of survey respondents citing customer satisfaction as a key goal.
In today’s consumer-driven landscape, however, companies must prioritize customer retention and loyalty as they work to enhance their customer experience management (CEM) initiatives. In fact, in just a few short years, 89% of businesses will compete mainly on the basis of customer experience.
Enter predictive analytics.
As noted by IBM, predictive analytics allow companies to “target the right customers, identify dissatisfied customers by uncovering patterns of buying behavior, and address customer service issues faster by correlating and analyzing a variety of data.”
The bottom line: Predictive analytics provides greater visibility into customer intent and behavior.
Let’s take a look at why you need to add predictive analytics to your CEM toolbox:
According to Aberdeen research data, predictive analytics users enjoy (as compared to non-users):
The above numbers speak for themselves, but consider one additional data point from Aberdeen:
Businesses are losing an average of $1.1 million each year because employees cannot access customer insights efficiently.
With predictive analytics in place, your organization can take a proactive approach to customer retention – as opposed to a reactive approach. In other words, you can leverage predictive analytics to learn from your mistakes and correct them in order to retain customers.
As noted by Call Centre Helper, speech analytics provides a “particularly rich source of information, because it conveys what’s on the mind of the customer in words and emotion. Combining these insights with the other source data adds context to form a more robust predictive model.”
Stated simply, the more information that can be fed into a predictive analytics model, the more accurate the model becomes. That’s where speech analytics comes into play.
CallMiner’s new and improved Eureka speech analytics immediately reveals insights from automated analysis of communications between you and your customers across multiple channels – including calls, chat, email, texts, social media, surveys and more. It also converts customer interactions into a format for analysis: evaluates every contact for sentiment/acoustics, categorization, and performance scoring; and determines Root cause through auto topic analysis.
With CEM increasing in importance in the years to come, proactive organizations will find ways to leverage predictive analytics to provide their customers with a satisfying experience. As the research above indicates, doing so will soon become the differentiator between successful companies and those struggling to retain their customer base.
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